Mark Hammond wrote: >> That is high relative to the conventional wisdom, but I'm questioning >> the correctness of that wisdom. >> > > Check out this thread, which should give you a reasonable idea: > > http://mail.python.org/pipermail/spambayes-dev/2003-November/001578.html > That thread was interesting, but still runs under the assumption that balanced training is the ideal. > >> Perhaps its time to re-evaluate that statement? >> > > Google also shows anecdotal reports of poor results after an imbalance as > low as 2:1, so I don't think it would be responsible to re-evaluate that > "responsible"? I'm not sure what you mean. > statement until clear evidence was presented to the contrary. > I assumed that running a test to evaluate the effects of imbalance would be the way to generate or refute such evidence? When I get back from Hawaii, I think I'll dust off the old test corpus and try some tests. If anybody else has some test results, I'd be very interested in seeing them.
My current thought is that getting a (very) large mount of spam with very few clues results in each email results in the imbalance. I've just checked some of todays spam and some had as few as 31 clues. With so few clues, it is relatively easy for a spam message to end up with an unsure or even ham classification while the most ham is being correctly classified. The alternative to an imbalanced training set is to find an easy way to train on extra ham, but only the ham that still has some classification value to add. Brendon. _______________________________________________ SpamBayes@python.org http://mail.python.org/mailman/listinfo/spambayes Check the FAQ before asking: http://spambayes.sf.net/faq.html
